Causal knowledge is fundamental to many domains of science, medicine and economics. This is due to the fact that causal relations, unlike correlations, allow one to reason counterfactually and to analyse the consequences of interventions. While powerful approaches to discovering causal relations between multiple variables in the absence of randomised controlled trials have been developed, many of these require all variables to be jointly measured and recorded in a single dataset. In many domains this assumption does not hold, due to ethical concerns, or financial and technological constraints.
For instance, in certain countries medical variables could be censored differently, meaning there is only access to joint measurements of certain variables, wherein joint measurements refer to measurements recorded at the same time. In another example, distinct medical sensors may measure differently, but overlapping aspects of a particular disease or physiological function. In another example, countries may report country-specific economic variables as well as those reported by other nations, due to specific financial reporting practices. In these examples, multiple datasets are provided, each recording a potentially different, but overlapping, set of variables.